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G ábor I. Veres CERN Geneva and ELTE Budapest CMS Collaboration. Based on: - JHEP 1009:091,2010 - My talk at ECT* Trento: QCD at the LHC, September 28, 2010 - CERN seminar talks by Gunther Roland and Guido Tonelli, Sept. 21, 2010.

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Gábor I. Veres

CERN Geneva and ELTE Budapest

CMS Collaboration

Based on:

- JHEP 1009:091,2010

- My talk at ECT* Trento: QCD at the LHC, September 28, 2010

- CERN seminar talks by Gunther Roland and Guido Tonelli, Sept. 21, 2010

Zimányi 2010 Winter School on Heavy Ion Physics, 29 Nov 2010, RMKI/ELTE Budapest

  • The CMS detector
  • Two-particle correlation functions
  • Results in minimum bias collisions
  • Results in high multiplicity collisions
  • Cross-checks
the cms silicon tracker
The CMS Silicon Tracker

Coverage up to||<2.5; extremely high granularity, to keep low occupancy (~ a few%) also at LHC nominal luminosity.

Largest Silicon Tracker ever built: Strips: 9.3M channels; Pixels: 66M channels.Operational fractions: strips 98.1%; pixel 98.3%









angular correlation functions
Angular Correlation Functions

I. Definition

Correlation Functions:

II. Anatomy

correlation function definition
Correlation Function Definition

Signal distribution:

Background distribution:

Same event pairs

Mixed event pairs

Ratio Signal/Background

pT-inclusive two-particle

angular correlations in

minimum bias collisions

CMS pp 7TeV

angular correlation functions1
Angular Correlation Functions

“Away-side” (Df ~ p) jet correlations:

Correlation of particles between back-to-back jets

CMS 7TeV pp min bias

Bose-Einstein correlations:

(Df ,Dh) ~ (0,0)

Momentum conservation:

~ -cos(Df)

Short-range correlations (Dh < 2):

Resonances, string fragmentation,


“Near-side” (Df ~ 0) jet peak:

Correlation of particles

within a single jet

correlations in min bias pp
Correlations in Min Bias pp

CMS pp Data

Pythia D6T

short range correlations vs s
Short-Range Correlations vs. s

Keff: Number of correlated particles

d: correlation width in Dh

1D “Projection” to Dh axis


PYTHIA describes the energy dependence,

matches cluster width d in data,

but underestimates the cluster multiplicity Keff


high multiplicity events
High Multiplicity Events

268 reconstructed particles in the tracker in a single pp collision:

the highest multiplicity event in ~70 billion inelastic events sampled (1/pb)

why study extreme multiplicities
Why study extreme multiplicities?
  • Our most recent correlation studies focus on the tail of the distribution, where several MC generators severely under-estimate the data
  • (an exception: PYTHIA8).
  • Motivations:
  • Trying to find (more) unexpected effects in this regime
  • Learn more about (soft) QCD and particle production mechanisms with more differential measurements
  • Highest multiplicities in pp begin to approach those in ion collisions; can we learn something about similarities or differences?
high multiplicity trigger
High Multiplicity Trigger

Dedicated trigger was needed to record highest multiplicities

Level-1 (hardware):

Requires ET> 60 GeV

in calorimeters

High-Level trigger (software):

More than 70 (85) tracks with pT > 0.4 GeV/c, |h| < 2, within dz < 0.12 cm of a single vertex with z < 10 cm.

~50% CPU usage of the HLT

high multiplicity event statistics
High Multiplicity Event Statistics

1000 times more

high multiplicity events

recorded with the trigger

compared to Min. Bias

Multiplicity binning uses

pT > 0.4 GeV/c

|Dh| < 2.4

Two different HLT thresholds:

Nonline > 70 and Nonline > 85

HLT85 trigger range un-prescaled

for full 980nb-1

out of 5x1010 collisions

event and track selection
Event and Track Selection

Event-selection and analysis done with tracks pointing to primary vertex with O(100mm) resolution

results data inclusive p t
Results : data, inclusive pT

high multiplicity (N>110)


Jet peak/away-side correlations enhanced in high multiplicity events

Abundant jet production in high multiplicity sample

results data inclusive p t1
Results: data, inclusive pT

high multiplicity (N>110)


After cuting off the jet peak at (0,0) we can observe:

Structure of away-side ridge (back-to-back jets)

Small change for large dh around df ~ 0 ?

results data p t 1 3 gev c
Results: data, pT: 1-3 GeV/c

high multiplicity (N>110)


Pronounced new structure at large dh, around df ~ 0 !

CMS Collab., JHEP 1009:091,2010, arXiv:1009:4122

illustration of the effect
Illustration of the effect




Particles surfacing in the same time zone, but far away in latitude, talk to each other…

…What mechanism is the “telephone line”?

correlations in pythia8
Correlations in PYTHIA8

Nodf ~0 structure at large dh

 Same for Herwig++, madgraph, PYTHIA6

multiplicity and p t dependence
Multiplicity- and pT -Dependence

Increasing pT

Increasing multiplicity

Project |Dh| > 2

onto Df



“Ridge” maximal for highest multiplicity and 1 < pT < 3 GeV/c

quantifying the associated yield
Quantifying the associated yield

Zero Yield At Minimum (ZYAM)

  • Data
  • - PYTHIA8




Associated yield:

correlated multiplicity per particle

Minimum of R


Associated yield grows with increasing multiplicity

like sign vs unlike sign pairs
Like-Sign vs. Unlike-Sign Pairs

No dependence on relative charge sign

systematic uncertainties checks
Systematic Uncertainties, Checks

Statistical uncertainty negligibly small

However, the signal is subtle and unexpected

Estimate systematic uncertainties

Is there a way to fake the signal qualitatively?

systematic uncertainties
Systematic Uncertainties

+ bugs?

Test the complete chain

with data-driven checks!

Analysis code




CMS Event


+ efficiency, fakes

+ trigger efficiency, bias

+ detector noise, acceptance, efficiency

+ pile-up, beam backgrounds


analysis code
Analysis Code

Control analysis II

Standard analysis

Control analysis I




Independent code

Different definition of R

Different input file (skim)

Independent code

Same definition of R

Same input file (skim)

Ridge is seen with three independent analysis codes

reconstruction code
Reconstruction Code



Pixel-only tracks

3 hits in pixel detector

“HighPurity” tracks

Pixel + Silicon Strip tracker

(Largely) independent code

Independent detectors

Also: variation of tracking +vertexing parameters

trigger bias
Trigger bias

Min-bias trigger vs. high mult trigger

HLT 70 vs. HLT 85 for N > 110

Min bias trigger






No trigger bias seen from comparison of trigger paths

Ridge is seen using

min bias trigger + offline selection

detector symmetry
Detector:  -symmetry

Pair multiplicity distribution

for ||>2 and ||<1

Constrain one track to one f-octant

  • Signal
  • Background







Ridge is not caused by rare events with large # of pairs

Ridge is invariant under f rotation

detector uniformity in
Detector: uniformity in 

Ridge region shows no structure in h1 vs h2

event backgrounds
Event Backgrounds

Enrich the sample with beam-gas

and beam-scraping events

Reject beam background by veto

on fraction of low quality tracks

CMS preliminary

Increased beam scraping events

Increased beam halo

Standard event selection




No eff correction




CMS preliminary

Ridge region shows no sensitivity to beam background

Note: Analysis is done on HighPurity tracks

pileup collision events
Pileup collision events

Correlate tracks from high multiplicity vertex with tracks

from different collision (vertex) in same bunch crossing

N>110; 1 GeV/c


No background or noise effects

seen in cross-collision correlations

pileup of collision events
Pileup of collision events

Compare different run periods (fraction of pileup varies by x4-5)

Compare different vertex regions

(fraction of pile-up ~ dN/dvtxz)



CMS preliminary

CMS preliminary

CMS preliminary

Change in pileup fraction by factor 2-4

has almost no effect on ridge signal

Fraction of pileup


event pileup
Event Pileup

Track longitudinal and transverse impact parameter

(pT > 0.4 GeV/c)

Single-event track dz distribution

Pileup effects are suppressed due to excellent resolution

Track counting done with sdz, sdxyof O(100mm)

track photon correlations
Track-Photon Correlations

Use ECAL “photon” signal

Mostly single photons from p0’s

No efficiency, and pT, f smearing corrections



CMS preliminary




Note: photons reconstructed using “particle flow”

event reconstruction technique

photon photon correlations
Photon-Photon Correlations

Use ECAL “photon” signal

Mostly single photons from p0’s

No efficiency, and pT, f smearing corrections



CMS preliminary




Qualitative confirmation

Independent detector, independent reconstruction

systematic uncertainties1
Systematic Uncertainties

Analysis code




CMS Event


Each step tested with data-based checks

No indication of effect that would fake ridge signal

Conservative estimates of uncertainties on ridge associated yield

  • Study of short-range and long-range angular correlations in pp collisions with CMS at LHC
  • Observation of long-range, near-side correlations in high multiplicity events
    • Signal grows with event multiplicity
    • Effect is maximal in the 1 < pT < 3 GeV/c range
    • Not observed in low multiplicity events
    • Not observed in MC generators
  • This is a subtle effect in a complex environment – careful work is needed to establish physical origin